Abstract

. In this paper we describe a face recognition method based on PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The method consists of two steps: first we project the face image from the original vector space to a face subspace via PCA, second we use LDA to obtain a linear classifier. The basic idea of combining PCA and LDA is to improve the generalization capability of LDA when only few samples per class are available. Using FERET dataset we demonstrate a significant improvement when principal components rather than original images are fed to the LDA classifier. The hybrid classifier using PCA and LDA provides a useful framework for other image recognition tasks as well. 1 Introduction The problem of automatic face recognition is a composite task that involves detection and location of faces in a cluttered background, normalization, recognition and verification. Depending on the nature of the application, e.g. sizes of training and testing database, clutter...

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